A GAN model consists of two neural networks: generative and adversarial networks. The generative network converts the input data drawn from a Gaussian distribution to synthetic data. The adversarial network classifies the generated synthetic data as either real or synthetic. The objective of the adversarial network is to maximize the difference between the real and synthetic data, while the generative model aims to minimize this difference [1]}. GAN has been deployed in various domains for generating synthetic datasets including images [2]} and music [3]}. In the context of power systems, GAN has been used to generate missing PMU data [4]} or address the lack of realistic PMU time series data [5]}, [6]}, [7]}.
Different from these applications that aimed at enhancing the historical data, in this paper, the GAN model is extended for predicting the future PMU data. The prediction capability pursued by this work significantly differs from the previous applications. Hence, a different approach to adopting GAN is required to generate realistic future data and predict the system transient stability based on those data. This approach should: a) learn the spatial and temporal features of the transient data; b) predict the post-fault PMU data; and c) assess the transient stability of the system. The developed GAN-based TSA approach addresses these requirements for a fast and accurate stability assessment.
